title: Detection of temporal changes in psychophysiological data using statistical process control methods.
abstract: We consider the problem of detecting temporal changes in the functional state of human subjects due to varying levels of cognitive load using real-time psychophysiological data. The proposed approach relies on monitoring several channels of electroencephalogram (EEG) and electrooculogram (EOG) signals using the methods of statistical process control. It is demonstrated that control charting methods are capable of detecting changes in psychophysiological signals that are induced by varying cognitive load with high accuracy and low false alarm rates, and are capable of accommodating subject-specific differences while being robust with respect to differences between different trials performed by the same subject.
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA.
authors.forename: Jordan
authors.initials: J
authors.lastname: Cannon
authors.forename: Pavlo A
authors.initials: PA
authors.lastname: Krokhmal
authors.forename: Yong
authors.initials: Y
authors.lastname: Chen
authors.forename: Robert
authors.initials: R
authors.lastname: Murphey
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D011603:Psychophysiology / Q000379:methods; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors